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I am an applied economist and former Associate Professor at EPFL - the Swiss Federal Institute of Technology.
Previously, I have co-founded five companies and led organizations in a variety of roles – including President, Chief Technology Officer, Chief Science Officer, and Director of Development.
Currently, I am retired and dedicating time to mountaineering objectives and volunteer projects.
Testimonials from Previous Engagements
Testimonials from Previous Engagements
The Fast Track was an excellent blend of high-level strategy, technical details, and real-world applications. It helped me to understand Data Science in a much more concrete way. I will certainly direct others to come!
Sivakumar Narayanan, Executive VP of Marketing & Business Development
Uster Technologies
The course is like a Neural Network on steroids. Content is deep and wide, but very well explained, and delivered with passion by Ken and the team.
Roman Schafer, VP Digital Operations
ABB
The training provided a very good understanding of what Machine Learning really means. Now I know the steps that I have to take to analyse my data, build a model, tune the model, understand the outcome, and communicate the findings. But most importantly, it took away the fear that Machine Learning is a subject beyond my capabilities as a chemical (non-software) engineer - I gained the confidence that I can do this myself.
Isabelle Crauser, Head of Q-Operation
CSL Behring
Impressive lectures, demos, and exercises from the entire team.
Lorenzo Musto, Software Developer and Arch Team Leader
Inpeco
The Boot Camp has been the best continuous education course I’ve attended in a very long time. Amidst the current hype on ML and AI, it's good to hear from a neutral and expert source on the possibilities and the pitfalls of the field - not just on the conceptual level, but also down to the implementation. I would strongly recommend it to anybody who is getting into data science. They do a great job at teaching the essentials in an enlightening, fun and interactive way !
Jonathan Friedli, Senior Automotive Development Engineer
The training showed a lot of use cases of Data Science in a business-near context. Concepts were brought to you in the easiest possible way so that business people such as myself could take away the most from the course.
Simon Roth, Head of Accounts Payable
SBB
This very intensive course gave me a comprehensive overview of a vast and fast-moving domain. The engaging teaching by Prof. Younge and his team – with lectures and many hands-on exercises – helps you to understand the complicated concepts.
Hessel Brouwer, Head of Financial Control
Dätwyler
This week-long course is highly recommend for every manager, coordinator, analyst, engineer, consultant etc. who is - or will soon be - collaborating with experts in ML/AI. It delivers the essence of theory combined with practical coding exercises (down in the weeds of Data Science) on a state of the art campus. By adding cross-industry networking on top, the program was able to create a very sustainable learning momentum. Thank you Prof. Kenneth Younge & Team for providing an outstanding (and intensive) learning experience!
Severin Gygax, Data Driven Business Manager
PostFinance AG
The Boot Camp has been the best continuous education course I’ve attended in a very long time. Amidst the current hype on ML and AI, it's good to hear from a neutral and expert source on the possibilities and the pitfalls of the field - not just on the conceptual level, but also down to the implementation. I would strongly recommend the course to anybody who is getting into data science. They do a great job at teaching the essentials in an enlightening, fun and interactive way !
Jonathan Friedli, Senior Automotive Development Engineer
The course provides an excellent mixture of theoretical background information, practical exercises, demos, and more management-oriented topics - all discussed and presented by highly competent course leaders. It is a great opportunity to put into context pre-existing knowledge, and enrich it with additional insights on recent developments and business applications. I am leaving with a much more solid, broader understanding of the potential of Data Science and the factors required to put it into practice within a corporate environment.
Thomas Kocher, Manager of Projects and R&D - Risk Engineering
SwissRe Corporate Solutions
The course allowed me to expand my knowledge on how to apply data science to real world situations. This is instrumental to better support my data science team and enhance our organization performance.
Gustavo Fernandez, Chief Executive Officer
Bridge
Excellent Data Science course. Intensive but you get your money's worth. I'd recommend it to anyone interested in the topic and not afraid to put their hands into the real thing.
Erwan Grasland, Chief Financial Officer
You learn deep down what it really takes to implement a Data Science project. For me, it was an eye opener. Now I have a better understanding of what is needed in terms of the people, knowledge, IT infrastructure, commitment, and dedicated budget to be successful. I'd recommend this course to anyone who needs to understand what it takes to enter the data science world. Professor Younge and his team are passionate about Data Science and are able to transmit their knowledge and experience in a very effective and fascinating way, with a lot of practical demos and exercises.
Federico Bernasconi, Head of Business Management, CEMT
Axpo Solutions
It was inspiring to see how modern technology can enable new insights from data. The training provided the impulse for self-transformation, encouraging me to leave my comfort zone and dive into a more quantitative field than the one in which I’m used to operating.
Martin Schlegel, Head Corporate Reporting
Straumann Group
A helpful course to look beyond the buzzwords of the industry and understand the key concepts, methodologies, applications, pitfalls, and limitations of the field. A good balance between theory and exercises. Recommend for those wanting to understand the details of data science for strategic planning.
Ivo dos Santos, Business Intelligence Analyst
Now definitively convinced that at the current level ML/AI can be a game changer for many of your businesses. It is at the root of a new era of productivity and process improvement.
Vladimiro Borsi, Viseca Card Services
I highly recommend the course! Prof. Younge gives an extensive overview of the field and the course helped me to improve how I work with our Machine Learning engineers. The highly-interactive nature of the demos and teaching assistants also help one to better understand the strengths and limitations of each algorithm. If you need to come up to speed quickly - so you can collaborate with a data science team or spot new opportunities for your business - this course is for you!
Alen Arslanagic, Chief Executive Officer
Visium
The bootcamp was an excellent, no-nonsense, hands-on eye-opener for me. Highly recommended if you also like to get your hands a wee bit dirty.
Markus Inderbitzin, Head IT Operations
It's been a busy week with long days of studying, but I can say it has been worthwhile. Learning about concepts of Data Science & AI paired with an understanding about how to evaluate and improve models can definitely help everyone working with our Einstein products. And beyond that it's great to get a perspective on how things can be done programmatically as we went through many hands-on exercises in Python.
Dennis Baeringhausen, Success Manager Analytics Cloud
Salesforce
Very pleased to have taken the boot camp this week. Great job, Kenneth Younge, Maximilian Hofer the whole team to achieve the quality of the course! Big respect for you data science guys out there!
Martin Frick, Chief Operating Officer (off duty)
Generali Switzerland
Great training, very good balance between strategic and technical levels. Perfect for managers willing to understand how to identify opportunities to implement Data Science in their business and how to build the proper environment/teams to drive the projects.
May Perrier
This week was hard! I was way out of my comfort zone and I definitively need to beef up my python skills - but it was probably the best invested week in a long time. The team does a fantastic job balancing theory and practice. I would recommend it to all who want to see the real deal and are prepared to suffer ;)
Patrick Deucher, Head of Sales
CSS Insurance
The course gave very good information on Data Science, Machine Learning and AI. The program gave plenty of opportunities to experience the concepts and tools with real life examples and data. Now I feel much more confident and better informed compared to where I was before the course. I am ready and looking forward to making progress with next steps!
Ilker Esener, Responsible for ERP, Data and Business Services
AC Immune
I got exactly what I have come for: the full picture and a deep overall understanding of the subject. The bootcamp is only one week, but the material is for months, both in terms of "know-how" but also "how-to."
Laurent Rossi, Head of MIS and financial controlling
I liked how the bootcamp offered participants a comprehensive toolkit (review of techniques, practical hands-on the code, strategy and mindset) to implement a data-driven strategy in their company. As a physicist, this helped me understand how machine-learning could be combined with my simulation activities.
Mikael
I see a lot of companies explain how AI can do magic, but it was important for me to learn how magical it really is. For a week, we opened the box of data science and saw that the engine needs to be fed with data -- good quality data! -- and then test test test before arriving at some insight. Much of the effort needs to focus on "how to get the good quality data." It remains a great opportunity, but it isn't magic anymore :-)
Sebastien Huet, Digital and data group director
Remy Cointreau
The training gave me a much better understanding of ML and AI in such a short time. Especially the combination of theory and the well prepared exercises for all the core concepts. Thanks a lot to Ken and his team for the great lectures and their support in this boot camp!
Ryan Held, Dufour Capital
Demystified AI for me. It showed the potential of machine learning, while also making clear its limitations. Ken is a truly inspiring instructor: he's fun and super intelligent. His strategic insights on business transformation are worth spending this very tough week at EPFL!
Régis Huf, Chief Executive Officer
Teaching
Teaching
I also am a dedicated teacher who has won outstanding teaching awards in 2011, 2014, 2015, 2016, 2017, 2020, and 2021.
I also am a dedicated teacher who has won outstanding teaching awards in 2011, 2014, 2015, 2016, 2017, 2020, and 2021.
Courses at EPFL:
Courses at EPFL:
Data Science for Business (MA)
Technology and Innovation Strategy (MA)
Computational Research Methods for Social Sciences (PhD)
Courses in the United States:
Courses in the United States:
Innovation Strategy (Exec Ed)
Technology Strategy (MBA)
Strategic Management (MBA)
Entrepreneurship and Business Plan Preparation (BA)
Research
Research
As an academic, my research has been published in The Review of Economics and Statistics, the IEEE, the US National Bureau of Economic Research, The Journal of Economics and Management Strategy, The RAND Journal of Economics, The Journal of Economic Behavior and Organization, and The Strategic Management Journal. Articles include:
Review of Economics and Statistics
Vol. 105, No. 2: pp. 458–466 ( 2023 )
Abstract: The United States patent system is unique in that it requires an applicant to cite documents they know to be relevant to the examination of their patent application. Lampe (2012) presents evidence that applicants strategically withhold 21-33% of relevant citations from patent examiners, suggesting that more than one in ten patents are fraudulently obtained. We challenge this view. We examine the institutional details of how courts identify strategic withholding and find that Lampe’s empirical design is inconsistent with both legal standards and standard operating procedures. We then compile a more up-to-date and detailed set of data to reassess the empirical basis for Lampe’s main claim. We find no evidence that applicants withhold citations from examiners.
Joint work with Jeffrey Kuhn and Alan Marco
Review of Economics and Statistics
Vol. 102, No. 3: pp. 569–582 ( 2020 )
Abstract: We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how individuals persist at exploration in an uncertain environment when confronted by prolonged periods of negative feedback. To examine this question, we design a two-dimensional maze game and run a series of randomized experiments with human subjects in the game. Our results suggest that individuals explore more when they are reminded of the incremental cost of their actions, a result that extends prior research on loss aversion and prospect theory to environments characterized by model uncertainty. In addition, we run simulations based on a model of reinforcement learning, that extend beyond two-period models of decision-making to account for repeated behavior in longer-running, dynamic contexts.
Joint work with Yaroslav Rosokha
The RAND Journal of Economics
Vol. 51, No. 1: pp. 109–132 ( 2020 )
Abstract: Many studies rely on patent citations to measure intellectual heritage and impact. In this article, we show that the nature of patent citations has changed dramatically in recent years. Today, a small minority of patent applications are generating a large majority of patent citations, and the mean technological similarity between citing and cited patents has fallen considerably. We replicate several well-known studies in industrial organization and innovation economics and demonstrate how generalized assumptions about the nature of patent citations have misled the field.
Joint work with Jeffrey Kuhn and Alan Marco
IEEE - 18th International Conference on Machine Learning and Applications
DOI 10.1109/ICMLA.2019.00120 ( 2019 )
Abstract: Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.
Joint work with Omid Shahmirzadi and Adam Lugowski
Journal of Economic Behavior & Organization
Vol. 150: pp. 162-181 ( 2018 )
Abstract: While executives play an important role in leading firm innovation, they may economize on efforts to innovate when protected from takeover threat. Middle managers may curtail the rate and scope of innovation when executives are expected to reduce their innovation involvement. We test our prediction by exploiting a natural experiment in Delaware where court rulings increased takeover protection for Delaware firms. Difference-in-differences estimates show that increased takeover protection reduced the rate of innovation by firms, and that it also reduced the scope of innovation across several key dimensions (technological, temporal, organizational, and international). Consistent with our argument, we find that the negative effect of takeover protection on innovation was weaker for larger firms, where innovation decision making authority is more likely to be delegated to middle managers and executive involvement is lower. Finally, we examine the substitutive relationship between competitive pressures from the takeover market and the product market, and find that the negative effect of takeover protection on innovation was stronger for firms facing low competitive pressure from the product market.
Joint work with Tony Tong
Journal of Economics & Management Strategy
Vol. 25: pp. 652-677 ( 2016 )
Abstract: We estimate the firm‐level returns to retaining employees using difference‐in‐differences analysis and a natural experiment where the enforcement of employee noncompete agreements was inadvertently reversed in Michigan. We find that noncompete enforcement boosted the short‐term value of publicly traded companies by approximately 9%. The effect is increasing in local competition and growth opportunities, and offset by patenting.
Joint work with Matt Marx
Strategic Management Journal
Vol. 36: pp. 686-708 ( 2015 )
Abstract: This study draws on strategic factor market theory and argues that acquirers' decisions regarding whether to bid for a firm reflect their expectations about employee departure from the firm post‐acquisition, suggesting a negative relationship between the anticipated employee departure from a firm and the likelihood of the firm becoming an acquisition target. Using a natural experiment and a difference‐in‐differences approach, we find causal evidence that constraints on employee mobility raise the likelihood of a firm becoming an acquisition target. The causal effect is stronger when a firm employs more knowledge workers in its workforce and when it faces greater in‐state competition; by contrast, the effect is weaker when a firm is protected by a stronger intellectual property regime that mitigates the consequences of employee mobility.
Joint work with Tony Tong and Lee Fleming
National Bureau of Economic Research
Adam Jaffe and Ben Jones, editors -- The Changing Frontier: Rethinking Science and Innovation Policy
Chapter 7: pp. 199 - 232 ( 2015 )
Abstract: We document three facts related to innovation and entrepreneurship in renewable energy. Using data from the US Patent and Trademark Office, we first show that patenting in renewable energy remains highly concentrated in a few large energy firms. In 2009, the top 20% firms accounted for over 40% of renewable energy patents in our data. Second, we compare patenting by venture capital-backed startups and incumbent firms. Using a variety of measures, we find that VC-backed startups are engaged in more novel and more highly cited innovations, compared to incumbent firms. Incumbent firms also have a higher share of patents that are completely un-cited or self-cited, suggesting that incumbents are more likely to engage in incremental innovation compared to VC-backed startups. Third, we document a rising share of patenting by startups that coincided with the surge in venture capital finance for renewable energy technologies in the early 2000s. We also point to structural factors about renewable energy that have led the availability of venture capital finance for renewable energy to fall dramatically in recent years, with potential implications for the rate and trajectory of innovation in this sector.
Joint work with Ramana Nanda and Lee Fleming
Wiley Blackwell Outstanding Dissertation Award ( 2013 )
Abstract: In this dissertation, I argue that employee mobility is a key consideration of the firm. Firms often rely on human assets to generate and maintain knowledge. When key individuals depart the firm, they take knowledge with them, potentially undermining the firm or helping competitors. Specifically, I theorize as to how the potential for employee departure affects firm value, and empirically examine my hypotheses in strategy contexts such as M&As, R&D, and equity investment.
Ph.D. Dissertation
National Renewable Energy Laboratory
NREL/TP-6A20-50624 ( 2011 )
Abstract: Low-carbon energy innovation is essential to combat climate change, promote economic competitiveness, and achieve energy security. Using U.S. patent data and additional patent-relevant data collected from the Internet, we map the landscape of low-carbon energy innovation in the United States since 1975. We isolate 10,603 renewable and 10,442 traditional energy patents and develop a database thatcharacterizes proxy measures for technical and commercial impact, as measured by patent citations and Web presence, respectively. Regression models and multivariate simulations are used to compare the social, institutional, and geographic drivers of breakthrough clean energy innovation. Results indicate statistically significant effects of social, institutional, and geographic variables ontechnical and commercial impacts of patents and unique innovation trends between different energy technologies. We observe important differences between patent citations and Web presence of licensed and unlicensed patents, indicating the potential utility of using screened Web hits as a measure of commercial importance. We offer hypotheses for these revealed differences and suggest a researchagenda with which to test these hypotheses. These preliminary findings indicate that leveraging empirical insights to better target research expenditures would augment the speed and scale of innovation and deployment of clean energy technologies.
Joint work with Thomas Perry, Mackay Miller, Lee Fleming, and James Newcomb
Climbing
Climbing
When I am not consulting, teaching, researching, or spending time with my family - I'm an avid climber who loves to get out, and climb up, whenever I can.
Curriculum Vitae
Curriculum Vitae
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